Numeracy and Data Analysis: Temperature Analysis of London
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This report provides a comprehensive analysis of the temperature of London using mean, median, mode, range, and standard deviation. It also includes a model of linear forecasting to calculate future temperatures. The report is useful for students studying numeracy and data analysis.
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Table of Contents INTRODUCTION...........................................................................................................................3 MAIN BODY...................................................................................................................................3 1. Presentation of information in tabular form.......................................................................3 2. Prepare the graphs for the data collected............................................................................4 3. Evaluate the mean, median, mode, range and standard deviation also make available the steps for analysing them.........................................................................................................4 4. Analyse the value of 'm' and 'c' and represent the steps to be followed. By the Use of 'm' and 'c' values, calculate the temperature for day 11 and day 14.............................................6 CONCLUSION................................................................................................................................8 REFERENCES................................................................................................................................9
INTRODUCTION The methods are a comprehensive part of illustrative statistics and important concept which is used in the processing of data with the steps (Almagtome, 2021). In the report, description of temperature of city of London, UK in 10 continuous days. A table is being maintained with the reporting of daily temperature with proper bars and line graphs. After that, the data for measuring the i.e. mean, median, mode, range and standard deviation. At the end of the report, model linear forecasting is being represented and evaluated which helps to calculate the future temperature of London. MAIN BODY 1. Presentation of information in tabular form. DAYTEMPERATURES 122 223 320 416 519 617 719 819 920 1021 TOTAL196
2. Prepare the graphs for the data collected. 3. Evaluate the mean, median, mode, range and standard deviation also make available the steps for analysing them. Mean:It is mathematical set of averages of two or more than two numbers.
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Steps to calculate Mean: Step1: Collection of the information. Step2: Summing up the advantages of set. Step3: Evaluating the data of numbers. Step4: Quarter up step 2 by step 3. Mean of Temperature= Addition of the given information/ Sum total set of data =196/10 =19.6 Median:It is number which is placed in centre and arranged in ascending or descending format and it can be more explanatory than set of data average. Below are the steps to calculate median: Step1: Sorting the data in ascending or descending order. Step2: Analysing all the odds and even number in the given data. Step3: Interpretation of formula (n+1) / 2 if the 'n' is odd or N / 2 if 'n' is even. Median Value (even) = N / 2 = 10/2 = 5thvalue 22,23,20,16,19,17,19,19,20,21 Median =19 Mode:It is the value that represents the appearance of set of data over and over again (Elbashir, 2021) Method of calculation of data: Step1: Maintaining the data in ascending or descending format. Step2: Enumerating the number of repeated digits. Step3: Analysing the numbers by looking them. Step4: Selection of the number which is the highest of them all Mode=19 & 20 Range:It is used to state the difference between highest and lowest values of a given set of data. Steps to calculate the ranges:
Step1: Analyse the given data. Step2: Selection of the highest or lowest value. Step3: Deduct them. Evaluating the Range of temperature: Range= Highest – Lowest value = 23-16 =7 Standard Deviation:It is statistical data which is used to measure the diversion of the set of data which is interrelated to mean and can be evaluated by the square root of the variation (Topor, 2021). Steps to calculate the standard deviations: Step1: Start with the value of mean. Step2: Find divergence from the mean. Step3: Squaring of all deviations and finding the total sum. Step4: Diverging the square from the total number of data. Step5: Make square root of the outcome for the result. Evaluation of standard deviation of temperature: Standard deviation=√∑ (xi – μ) ^ 2 / N 4. Analyse the value of 'm' and 'c' and represent the steps to be followed. By the Use of 'm' and 'c' values, calculate the temperature forday 11 and day 14. Linear Forecasting Model: It is a statistical tool which is used to estimating future values on the basis of past values (Weigand, Blums and Kruijff, 2018). Steps to analyses the model are: Step1: Evaluate the problem. Step2: Collection of data should be done on the basis of survey. Step3: Select the model which is most acceptable form. Step4: Evaluation of the problem should be done carefully. Y =mx + C
where,'y' is the Dependent Factor, 'mx' is the Independent factor and 'c' is a constant factor. Steps to calculate 'm': Step1: Multiplication of total number of data with the variables of 'x' and 'y'. Step2: Calculation of the sum of 'x' and 'y' and individually multiply them. Step3: Evaluating the square of x with the total number of the data. Step4: Calculating the sum of 'x' and squaring it up. Step5: Deducting the step 2 from step 1. Step6: After that, subtracting step 4 and step 3. Step 7: In the end, Divergence of the value of Step 5 with Step 6. = [(10* 1064) – (55 * 196)] / (10 * 385) – (55)2 = [10640 – 10780]/3850-110 = -140/3740 = -0.037 Method of calculating 'c’:- Step1: Adding the values of 'y' variable. Step2: Multiplying the value 'm' with the addition of the values of the 'x' variable. Step4: Evaluating the difference between Step2 and Step1. Step5: Evaluating the number of values (Youssef and Mahama, 2021). Step6: Then dividing the outcome of step3 by step5. =196*0.037(55)/10 C= 39.886 Temperature of Day 11:
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m= 0.037, x=11, C =39.886 y = mx + C 0.037*11+39.886 y= 40.293 Temperature of Day 14: m= 0.037, x=11, C= 40.293 y = mx + C =0.037*14+40.293 y = 40.811 CONCLUSION The table is being prepared for showing the temperature of 10 days with the help of proper maintenance of bar graph, line graph which shows the data. The above calculation shows the analysing of statistics of mean, median, mode, range and standard deviation of the city London which provides the information in accordance to the averaging value of temperature and represents where the data is located. In addition to find the temperature of day 11 and day 14, the model of linear forecasting is being used for calculating the future temperatures of the city.
REFERENCES Books and Journals Almagtome,A.H.,2021.ArtificialIntelligenceApplicationsinAccountingandFinancial Reporting Systems: An International Perspective. InHandbook of Research on Applied AI for International Business and Marketing Applications(pp. 540-558). IGI Global. Elbashir, M.Z., and et.al., 2021. Unravelling the integrated information systems and management controlparadox:enhancingdynamiccapabilitythroughbusiness intelligence.Accounting & Finance,61. pp.1775-1814. Kwilinski, A., 2019. Implementation of blockchain technology in accounting sphere.Academy of Accounting and Financial Studies Journal,23. pp.1-6. Topor, D.I., and et.al.,2021.E-Accounting:FutureChallengesand Perspectives.CSR and Management Accounting Challenges in a Time of Global Crises. pp.35-52. Weigand, H., Blums, I. and Kruijff, J.D., 2018, June. Shared ledger accounting-implementing the economic exchange pattern in DL technology. InInternational Conference on Advanced Information Systems Engineering(pp. 342-356). Springer, Cham. Youssef, M.A.E.A. and Mahama, H., 2021. Does business intelligence mediate the relationship betweenERPandmanagementaccountingpractices?.JournalofAccounting& Organizational Change.